Earth Observation for Citizen Science Validation, or, Citizen Science for Earth Observation Validation? The Role of Quality Assurance of Volunteered Observations

Leibovici, Didier; Williams, Jamie; Rosser, Julian F.; Hodges, Crona; Chapman, Colin A.; Higgins, Chris; Jackson, Mike

doi:10.20944/preprints201710.0016.v1

Cited by 4 publications

(5 citation statements)

References 29 publications

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“…Proper training, data-collection protocols and standardizations can help to generate high-quality, reliable data for formal analysis and integration, even for data from non-traditional sources. Moreover, citizen science data and geospatial data can be used for cross-validating each other, which has already been demonstrated by Leibovici et al (2017) to be useful through an automated quality-assurance workflow.…”

Section: The Way Forwardmentioning

confidence: 99%

Building scientific capacity in disaster risk reduction for sustainable development

Chen

Shirazi

Wang

2021

Cultures of Science

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As climate warming intensifies, the frequency and intensity of disasters are also increasing, posing challenges to global sustainable development. The concept of disaster risk reduction (DRR) provides strong impetus for reducing disaster risk and vulnerabilities by employing the scientific and technological developments of recent decades. However, there is a need to enhance the capacities of different communities to use emerging digital infrastructure, not only in promoting DRR but also in ensuring sustainable future development. Limited access to and availability of data are restricting comprehensive understanding of these challenges. In many countries, the key areas for capacity development include collecting information from alternative and emerging data sources and meaningfully integrating it with data from traditional sources. Software and data analysis are becoming widely accessible due to open-source initiatives, while cloud computing technologies and programmes such as CASEarth provide valuable resources for multisource data integration, contributing to information-driven policy and decision-support systems for DRR.

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Section: The Way Forwardmentioning

confidence: 99%

Building scientific capacity in disaster risk reduction for sustainable development

Chen

Shirazi

Wang

2021

Cultures of Science

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“…Table 1. Quality elements for the COBWEB (Citizens Observatory WEB) stakeholder quality (CSQ) model [9,10].…”

Section: What Is Good and Bad Quality In Crowdsourcing For Environmenmentioning

confidence: 99%

“…A considerable body of literature has discussed data quality issues associated with VGI data, along with their spatial and a-spatial characteristics [4,5]. Specificities of quality assurance (QA) when collecting crowdsourced or citizen science data [6][7][8][9][10] are contributing to better designs on how to qualify data as a single observation or as part of a dataset. Quality assurance (QA) is defined as a set of data policy, controls and tests put in place in order to be able to meet specific requirements.…”

Section: Introductionmentioning

confidence: 99%

“…We frame our discussion using the framework elaborated in the COBWEB (Citizens Observatory WEB) project and the experience gained from that [11]. The European FP7 COBWEB project (www.cobwebproject.eu) proposed a survey design tool including an authoring tool to combine different quality controls (QC) within a workflow that will serve as a QA for each particular case study [9][10][11][12]; the data collected and then qualified through the QA workflow is finally made available for a DCDF within a completed data curation process [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

“…Within the COBWEB framework these workflows are represented using the Business Process Modelling Notation (BPMN) standard and are composed of automated processes [9,11,15,16]. QA is devoted to ensuring data quality and produces metadata information either at the single record or full dataset level.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On Data Quality Assurance and Conflation Entanglement in Crowdsourcing for Environmental Studies

Leibovici

Rosser

Hodges

et al. 2017

IJGI

Self Cite

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Volunteer geographical information (VGI), either in the context of citizen science or the mining of social media, has proven to be useful in various domains including natural hazards, health status, disease epidemics, and biological monitoring. Nonetheless, the variable or unknown data quality due to crowdsourcing settings are still an obstacle for fully integrating these data sources in environmental studies and potentially in policy making. The data curation process, in which a quality assurance (QA) is needed, is often driven by the direct usability of the data collected within a data conflation process or data fusion (DCDF), combining the crowdsourced data into one view, using potentially other data sources as well. Looking at current practices in VGI data quality and using two examples, namely land cover validation and inundation extent estimation, this paper discusses the close links between QA and DCDF. It aims to help in deciding whether a disentanglement can be possible, whether beneficial or not, in understanding the data curation process with respect to its methodology for future usage of crowdsourced data. Analysing situations throughout the data curation process where and when entanglement between QA and DCDF occur, the paper explores the various facets of VGI data capture, as well as data quality assessment and purposes. Far from rejecting the usability ISO quality criterion, the paper advocates for a decoupling of the QA process and the DCDF step as much as possible while still integrating them within an approach analogous to a Bayesian paradigm.

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Modelling Physical Accessibility to Public Green Spaces in Switzerland to Support the SDG11

2021

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Urban sprawl has a strong impact on the provision and use of green spaces and, consequently, on the benefits that society can derive from these natural ecosystems, especially in terms of public health. In looking at the Sustainable Development Goals and other regional policy frameworks, there is a strong need for quantifying access to green spaces. This study presents and applies a methodology to model the physical accessibility at national and sub-national scales to public green spaces (i.e., urban green spaces and forests) in Switzerland, using AccessMod and ArcGIS travel time functions. We found that approximately 75% and 36% of the Swiss population can access the nearest urban green space within 5 min and 15 min, respectively, using motorized transport. For motorized access to the nearest forest patch, 72% and 52% of the population are within 5 min and 15 min, respectively. When considering only the main urban areas, approximately 55% of the population can walk to the nearest urban green space within 5 min. However, a high heterogeneity in access exists at cantonal and municipal levels, depending on road density, green space density, and population distribution. Despite some possible challenges in correctly delineating public green spaces, our methodology offers a replicable approach offering not only insights into sustainable urban development, but also the facilitation of comparison with other European countries.

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Earth Observation for Citizen Science Validation, or, Citizen Science for Earth Observation Validation? The Role of Quality Assurance of Volunteered Observations

Cited by 4 publications

References 29 publications

Building scientific capacity in disaster risk reduction for sustainable development

Building scientific capacity in disaster risk reduction for sustainable development

On Data Quality Assurance and Conflation Entanglement in Crowdsourcing for Environmental Studies

Modelling Physical Accessibility to Public Green Spaces in Switzerland to Support the SDG11

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